Based on solving an equivalent parametric equality constrained mini-max
problem of the classic logarithmic-barrier subproblem, we present a novel
primal-dual interior-point relaxation method for nonlinear programs. In the
proposed method, the barrier parameter is updated in every step as done in
interior-point methods for linear programs, which is prominently different from
the existing interior-point methods and the relaxation methods for nonlinear
programs. Since our update for the barrier parameter is autonomous and
adaptive, the method has potential of avoiding the possible difficulties caused
by the unappropriate initial selection of the barrier parameter and speeding up
the convergence to the solution. Moreover, it can circumvent the jamming
difficulty of global convergence caused by the interior-point restriction for
nonlinear programs and improve the ill conditioning of the existing primal-dual
interiorpoint methods as the barrier parameter is small. Under suitable
assumptions, our method is proved to be globally convergent and locally
quadratically convergent. The preliminary numerical results on a well-posed
problem for which many line-search interior-point methods fail to find the
minimizer and a set of test problems from the CUTE collection show that our
method is efficient.Comment: submitted to SIOPT on April 14, 202